1 citations · 2 across the 10 of their papers we have counts for
10 papers
Omni-DuplexEval: Evaluating Real-time Duplex Omni-modal Interaction
Chaoqun He, Mingyang Xiang, Yingjing Xu +5
Real-time duplex interaction is essential for multimodal AI systems operating in real-world scenarios, where models must continuously process streaming inputs and respond at approp…
Efficient Spatio-Temporal Grounding with Multimodal Large Models via Second-Level Tracking and RL Verification
Tianshu Zhang, Yan Wang, Ji Qi +1
Spatio-temporal grounding in long videos requires precise temporal localization and robust object tracking conditioned on natural-language queries. While recent vision-language mod…
VaaWIT: Visual-Aware Adaptation of Large Language Models for Multilingual Web Image Translation
Bo Li, Ronghao Chen, Ningyuan Deng +3
Translating text embedded in Web images is crucial for improving content accessibility and cross-lingual information retrieval, particularly within social media and e-commerce doma…
Do We Really Need External Tools to Mitigate Hallucinations? SIRA: Shared-Prefix Internal Reconstruction of Attribution
Tian Qin, Junzhe Chen, Yuqing Shi +3
Large vision-language models (LVLMs) often hallucinate when language priors dominate weak or ambiguous visual evidence. Existing contrastive decoding methods mitigate this problem…
MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation
Bo Li, Ningyuan Deng, Tianyu Dong +3
Multimodal large language models (MLLMs) have shown impressive capabilities, yet they often struggle to effectively capture the fine-grained textual information within images cruci…
Meta-TTRL: A Metacognitive Framework for Self-Improving Test-Time Reinforcement Learning in Unified Multimodal Models
Lit Sin Tan, Junzhe Chen, Xiaolong Fu +5
Existing test-time scaling (TTS) methods for unified multimodal models (UMMs) in text-to-image (T2I) generation primarily rely on search or sampling strategies that produce only in…
DR-LoRA: Dynamic Rank LoRA for Fine-Tuning Mixture-of-Experts Models
Guanzhi Deng, Bo Li, Ronghao Chen +5
Mixture-of-Experts (MoE) has become a prominent paradigm for scaling Large Language Models (LLMs). Parameter-efficient fine-tuning methods, such as LoRA, are widely adopted to adap…
REMA: A Unified Reasoning Manifold Framework for Interpreting Large Language Model
Bo Li, Guanzhi Deng, Ronghao Chen +5
Understanding how Large Language Models (LLMs) perform complex reasoning and their failure mechanisms is a challenge in interpretability research. To provide a measurable geometric…
MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation
Bo Li, Shaolin Zhu, Lijie Wen
Image Translation (IT) holds immense potential across diverse domains, enabling the translation of textual content within images into various languages. However, existing datasets…
ROME: Memorization Insights from Text, Logits and Representation
Bo Li, Qinghua Zhao, Lijie Wen
Previous works have evaluated memorization by comparing model outputs with training corpora, examining how factors such as data duplication, model size, and prompt length influence…